> ## Documentation Index > Fetch the complete documentation index at: https://docs.recallrai.com/llms.txt > Use this file to discover all available pages before exploring further. # Core Concepts Overview > Understand the fundamental building blocks of Recallr AI ## Understanding Recallr AI Recallr AI is built around a few core concepts that work together to provide persistent memory for AI applications. This page introduces these concepts and shows how they relate to each other. ## The Four Main Components Individual people using your application, each with isolated memory Conversations or interactions that contain messages Extracted facts and knowledge stored long-term Intelligent handling of contradictory information ## How They Work Together Think of Recallr AI like a personal assistant's notebook: ```mermaid theme={null} graph TB A[User] --> B[Has Many Sessions] B --> C[Session 1: Monday Chat] B --> D[Session 2: Wednesday Chat] B --> E[Session 3: Friday Chat] C --> F[Process] D --> F E --> F F --> G[Memories Extracted] G --> H[User Memory Graph] H --> I[Used for Future Context] ``` Each individual using your app (customer, player, student, etc.) is a **User**. They have: * A unique ID (you provide this) * Metadata (name, email, preferences, etc.) * A collection of memories (automatically extracted from conversation **sessions**) * Can have multiple sessions over time When a User interacts with your app, it happens in a **Session**. A session: * Contains messages (back-and-forth dialogue) * Has a status (pending, processing, processed) * Will be processed to extract memories * Represents one conversation or interaction When you process a session, Recallr AI analyzes the conversation and creates **Memories**: * Structured facts about the user * Categorized automatically * Versioned (track changes over time) * Semantically searchable Sometimes new information conflicts with existing memories, creating **Merge Conflicts**: * Detect contradictions automatically (e.g., "I love coffee" vs. "I hate coffee") * **Disabled by default** - must be enabled in project settings * When enabled, generates clarifying questions sent via webhooks * You provide answers to resolve conflicts and update memories * Preserves accuracy by requiring human clarification for contradictions ## Real-World Analogy Imagine you're a human assistant taking notes about your clients: | Recallr AI Concept | Real-World Equivalent | | ------------------ | ---------------------------------------------------------------- | | **User** | A specific client you work with | | **Session** | One meeting or phone call with that client | | **Messages** | The back-and-forth conversation during the meeting | | **Memories** | Important facts you write in your notebook about the client | | **Merge Conflict** | When the client says something that contradicts your notes | | **Process** | After the meeting, reviewing your notes and updating them | | **Get Context** | Before a meeting, reviewing your notes to remember what you know | ## FAQs ### Users Create a user when: * A new person signs up for your app * A first-time visitor starts a conversation * You need to track someone's preferences Example: E-commerce customer, game player, student Good user IDs are: * Unique and stable (don't change over time) * From your existing system (database ID, email, UUID) * Easy to retrieve when needed ✅ Good: `user_12345`, `john@example.com`, `player_uuid_...` ❌ Bad: Changing usernames, timestamps, random numbers ### Sessions Start a session when: * User opens a chat window * User begins a new conversation * Significant time has passed since last interaction Example: New customer support ticket, new game level, new tutoring session Process a session when: * Chat window is closed * Extended period of inactivity ### Memories Recallr AI automatically extracts: * User preferences and dislikes * Personal information shared * Goals and plans * Factual statements about the user * Important context from conversations Example memories: * "User's birthday is March 15th" * "User is learning Python" ... ### Merge Conflicts Conflicts occur when: * New information contradicts existing memories * User changes their mind or preferences * Ambiguous or unclear statements are made **Example conflict:** * Existing memory: "User is vegetarian" * New message: "I ate chicken last night" * -> Merge Conflict created! Conflicts are resolved through webhook notifications: 1. **Enable merge conflicts** in your project settings 2. **Add webhook URL** to receive conflict events 3. **Receive webhook** with conflict details and clarifying questions 4. **Provide answers** via API to resolve the conflict 5. **Memories update** automatically based on your answers See the [Merge Conflicts guide](/concepts/merge-conflicts) for implementation details. Merge conflicts require webhook setup and human intervention, so they're disabled by default to: * Keep the default experience simple * Avoid overwhelming new users with complexity * Allow gradual adoption as your use case requires it Enable them when accuracy is critical and you can handle webhook responses. ## Architecture Diagram Here's how data flows through Recallr AI: ```mermaid theme={null} sequenceDiagram participant App as Your Application participant SDK as Recallr AI SDK participant API as Recallr AI API participant LLM as Your LLM App->>SDK: Create user SDK->>API: POST /users API-->>SDK: User created App->>SDK: Start session SDK->>API: POST /sessions API-->>SDK: Session created App->>SDK: Add message SDK->>API: POST /messages App->>SDK: Get context SDK->>API: GET /context API-->>SDK: Relevant memories SDK-->>App: Context text App->>LLM: Generate response with context LLM-->>App: Personalized response App->>SDK: Process session SDK->>API: POST /process API-->>SDK: Memories extracted ``` ## Next Steps Now that you understand the core concepts, dive deeper into each component: Learn about user management and metadata Understand session lifecycle and states Explore memory structure and versioning Handle contradictory information Optimize memory retrieval Get notified of events